Journal
PATTERN RECOGNITION, MCPR 2019
Volume 11524, Issue -, Pages 292-301Publisher
SPRINGER INTERNATIONAL PUBLISHING AG
DOI: 10.1007/978-3-030-21077-9_27
Keywords
3D digital images; Parallel computing; Abstract cell complex; Homological Spanning Forest; Crack transport
Funding
- Spanish research project TOP4COG
- AEI/FEDER,UE [MTM2016-81030-P]
- COFNET (AEI/FEDER,UE)
- VPPI of University of Seville
- Austrian Science Fund [FWF-P27516]
Ask authors/readers for more resources
Segmentations of a digital object based on a connectivity criterion at n-xel or sub-n-xel level are useful tools in image topological analysis and recognition. Working with cell complex analogous of digital objects, an example of this kind of segmentation is that obtained from the combinatorial representation so called Homological Spanning Forest (HSF, for short) which, informally, classifies the cells of the complex as belonging to regions containing the maximal number of cells sharing the same homological (algebraic homology with coefficient in a field) information. We design here a parallel method for computing a HSF (using homology with coefficients in Z/2Z) of a 3D digital object. If this object is included in a 3D image of m(1) x m(2) x m(3) voxels, its theoretical time complexity order is near O(log(m(1) + m(2) + m(3))), under the assumption that a processing element is available for each voxel. A prototype implementation validating our results has been written and several synthetic, random and medical tridimensional images have been used for testing. The experiments allow us to assert that the number of iterations in which the homological information is found varies only to a small extent from the theoretical computational time.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available